Sabanci-Okan system at ImageClef 2011: plant identication task

We describe our participation in the plant identication task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our rst attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our eorts

Offline signature verification using classifier combination of HOG and LBP features

We present an offline signature verification system based on a signature’s local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP). The classification is performed using Support Vector Machines (SVMs), where two different approaches for training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s signature from others, whereas a single global SVM trained with difference vectors of query and reference signatures’ features of all users, learns how to weight dissimilarities. The global SVM classifier is trained using genuine and forgery signatures of subjects that are excluded from the test set, while userdependent SVMs are separately trained for each subject using genuine and random forgeries. The fusion of all classifiers (global and user-dependent classifiers trained with each feature type), achieves a 15.41% equal error rate in skilled forgery test, in the GPDS-160 signature database without using any skilled forgeries in training

A Morphology-Aware Network for Morphological Disambiguation

Agglutinative languages such as Turkish, Finnish andHungarian require morphological disambiguation beforefurther processing due to the complex morphologyof words. A morphological disambiguator is usedto select the correct morphological analysis of a word.Morphological disambiguation is important because itgenerally is one of the first steps of natural languageprocessing and its performance affects subsequent analyses.In this paper, we propose a system that uses deeplearning techniques for morphological disambiguation.Many of the state-of-the-art results in computer vision,speech recognition and natural language processinghave been obtained through deep learning models.However, applying deep learning techniques to morphologicallyrich languages is not well studied. In this work,while we focus on Turkish morphological disambiguationwe also present results for French and German inorder to show that the proposed architecture achieveshigh accuracy with no language-specific feature engineeringor additional resource. In the experiments, weachieve 84.12 , 88.35 and 93.78 morphological disambiguationaccuracy among the ambiguous words forTurkish, German and French respectively

Automatic plant identification from photographs

We present a plant identification system for automatically identifying the plant in a given image. In addition to common difficulties faced in object recognition, such as light, pose and orientation variations, there are further difficulties particular to this problem, such as changing leaf shapes according to plant age and changes in the overall shape due to leaf composition. Our system uses a rich variety of shape, texture and color features, some being specific to the plant domain. The system has achieved the best overall score in the ImageCLEF’12 plant identification campaign in both the automatic and human-assisted categories. We report the results of this system on the publicly available ImageCLEF’12 plant dataset, as well as the effectiveness of individual features. The results show 61 and 81 % accuracies in classifying the 126 different plant species in the top-1 and top-5 choices

Activity Recognition Using a Hierarchical Model

Abstract—In this paper, we propose a human daily activity recognition method that is used for Ambient Assisted Living. The proposed system is able to learn a user’s activities using the data from motion and door sensors. We extract low level features from the sensor data and feed the features to a model that combines support vector machines (SVMs) and conditional random fields (CRFs) to give accurate recognition results. We propose to combine SVM and CRF classifiers in a hierarchical model which results in better accuracies and can also make use of high level features. We conducted experiments and presented the effectiveness and accuracies of the proposed method. I

Sabanci-Okan system at ImageClef 2012: combining features and classifiers for plant identification

We describe our participation in the plant identication task of ImageClef 2012. We submitted two runs, one fully automatic and another one where human assistance was provided for the images in the photo category. We have not used the meta-data in either one of the systems, for exploring the extent of image analysis for the plant identication problem. Our approach in both runs employs a variety of shape, texture and color descriptors (117 in total). We have found shape to be very discriminative for isolated leaves (scan and pseudoscan categories), followed by texture. While we have experimented with color, we could not make use of the color information. We have employed the watershed algorithm for segmentation, in slightly dierent forms for automatic and human assisted systems. Our systems have obtained the best overall results in both automatic and manual categories, with 43% and 45% identication accuracies respectively. We have also obtained the best results on the scanned image category with 58% accuracy

Memory conscious sketched symbol recognition

Automatic sketch recognition is used to enhance human-computer interaction by allowing a natural/free form of interaction. It is a challenging problem due to the variability in hand drawings, the variation in the order of strokes, and the similarity of symbol classes. Since sketch recognition requires real time processing, the speed of the classifier is important. Another important issue is how to deal with very large data sets and/or large number of classes, as these also effect training and testing speed, making certain approaches infeasible. In order to deal with these issues, we present a memory conscious sketch recognition system that processes the data to retain only a few templates per class as prototypes; and furthermore, the query and prototypes are subsampled without loosing important information. The system also uses a cascaded combination of classifiers, to improve speed, as well as increase recognition accuracy. Results obtained using the public COAD and NicIcon databases are comparable to previous results obtained for these databases

Sketched symbol recognition with few examples using particle filtering

Sketching is a natural form of user interface that is employed in a variety of areas, such as engineering drawings or classroom teaching. Recognition of hand drawn sketches is a challenging problem due to the variability in hand drawing, variability in the drawing order of strokes, and the similarity of sketch classes. In this work, we present a system that can classify hand drawn symbols using few examples. The quality of the alignment between two symbols is measured and used to asses the similarity between these symbols. We present recognition experiments using the COAD and the NicIcon databases with promising results

Sketched symbol recognition with auto-completion

Sketching is a natural mode of communication that can be used to support communication among humans. Recently there has been a growing interest in sketch recognition technologies for facilitating human-computer interaction in a variety of settings, including design, art, and teaching. Automatic sketch recognition is a challenging problem due to the variability in hand drawings, the variation in the order of strokes, and the similarity of symbol classes. In this paper, we focus on a more difficult task, namely the task of classifying sketched symbols before they are fully completed. There are two main challenges in recognizing partially drawn symbols. The first is deciding when a partial drawing contains sufficient information for recognizing it unambiguously among other visually similar classes in the domain. The second challenge is classifying the partial drawings correctly with this partial information. We describe a sketch auto-completion framework that addresses these challenges by learning visual appearances of partial drawings through semi-supervised clustering, followed by a supervised classification step that determines object classes. Our evaluation results show that, despite the inherent ambiguity in classifying partially drawn symbols, we achieve promising auto-completion accuracies for partial drawings. Furthermore, our results for full symbols match/surpass existing methods on full object recognition accuracies reported in the literature. Finally, our design allows real-time symbol classification, making our system applicable in real world applications